Prediction of extreme cargo ship panel stresses by using deconvolution

Abstract

Extreme value predictions typically originate from certain functional classes of statistical distributions to fit the data and are subsequently extrapolated. This paper describes an alternative method for extrapolation that is based on the intrinsic properties of the data set itself and that does not pre-assume any extrapolation functional class. The proposed novel extrapolation method can be utilized in engineering design. To illustrate this, this study uses two examples to showcase the advantages of the proposed method. The first example used synthetic data from a non-linear Duffing oscillator to illustrate the new method. The second example was an actual container ship sailing between Europe and America and experiencing large deck panel stresses in severe weather. In this example, actual onboard measured data were used in the present study. This example represents a real and physical case that is challenging to model due to the non-stationary and highly non-linear natures of the wave-ship load responses. This is especially so in the case of extreme responses, where the roles of second and higher-order responses tend to be more prominent and have higher contributions. The prediction accuracy of the proposed method was also validated versus the Naess–Gaidai extrapolation method. Finally, this study discusses new methods for generic smoothing of distribution tail irregularities due to underlying scarcity in the data set.

Novel methods for wind speeds prediction across multiple locations

“…The latter is an unacceptable numerical inaccuracy since only positive numbers may represent distributions. To address this numerical difficulty, the following scaling method has been implemented 12 , 16 . As the pivot value, the lowest positive value of the specified distribution tail is used.…”

“…So having , one must concurrently and continuously screen for each unidimensional response component temporal maxima, noting its exceedances of MDOF limit vector in any of its unidimensional response components Each unidimensional response component maxima are then combined into a single temporal non-decreasing vector based on the merged time vector . Finally, the unified MDOF limit vector is now introduced with each component being either , or …, depending on which of or , or … corresponds to the current unidimensional response component maxima with the running index , see 12 – 16 .…”

“…. , η N ) is now introduced with each component η j being either η X , η Y or η Z …, depending on which of X(t) or Y (t) , or Z(t) … corresponds to the current unidimensional response component maxima with the running index j , see [12][13][14][15][16] .…”

Improving extreme offshore wind speed prediction by using deconvolution

“…The countless strategies endeavoured included the method presented by Mohande [ 26 ], which predicts wind speeds using neural networks or the different approaches to analyse wind speed estimates by Yan et al, as in Refs. [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Similarly, the authors of this paper have also previously used different statistical approaches to estimate better extreme statistical values such as, i.e.…”

“…Similarly, the authors of this paper have also previously used different statistical approaches to estimate better extreme statistical values such as, i.e. wind speed, wave height, response and load [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Furthermore, the authors developed a novel reliability approach to forecast COVID-19 epidemic levels, indicating the approach's versatility and reliability in various fields [ 14 , 16 , 26 ].…”